2 Python For Data Science Cheat Sheet NumPy Basics Learn Python for Data Science Interactively at www.DataCamp.com NumPy DataCamp Learn Python for Data Science Interactively The NumPy library is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. >>> import numpy as np Use the following import convention: Creating Arrays >>> np.zeros((3,4)) Create an array of zeros >>> np.ones((2,3,4),dtype=np.int16) Create an array of ones >>> d = np.arange(10,25,5) Create an array of evenly spaced values (step value) >>> np.linspace(0,2,9) Create an array of evenly spaced values (number of samples) >>> e = np.full((2,2),7) Create a constant array >>> f = np.eye(2) Create a 2X2 identity matrix >>> np.random.random((2,2)) Create an array with random values >>> np.empty((3,2)) Create an empty array Array Mathematics >>> g = a - b Subtraction array([[-0.5, 0. , 0. ], [-3. , -3. , -3. ]]) >>> np.subtract(a,b) Subtraction >>> b + a Addition array([[ 2.5, 4. , 6. ], [ 5. , 7. , 9. ]]) >>> np.add(b,a) Addition >>> a / b Division array([[ 0.66666667, 1. , 1. ], [ 0.25 , 0.4 , 0.5 ]]) >>> np.divide(a,b) Division >>> a * b Multiplication array([[ 1.5, 4. , 9. ], [ 4. , 10. , 18. ]]) >>> np.multiply(a,b) Multiplication >>> np.exp(b) Exponentiation >>> np.sqrt(b) Square root >>> np.sin(a) Print sines of an array >>> np.cos(b) Element-wise cosine >>> np.log(a) Element-wise natural logarithm >>> e.dot(f) Dot product array([[ 7., 7.], [ 7., 7.]]) Subseing, Slicing, Indexing >>> a.sum() Array-wise sum >>> a.min() Array-wise minimum value >>> b.max(axis=0) Maximum value of an array row >>> b.cumsum(axis=1) Cumulative sum of the elements >>> a.mean() Mean >>> b.median() Median >>> a.corrcoef() Correlation coefficient >>> np.std(b) Standard deviation Comparison >>> a == b Element-wise comparison array([[False, True, True], [False, False, False]], dtype=bool) >>> a < 2 Element-wise comparison array([True, False, False], dtype=bool) >>> np.array_equal(a, b) Array-wise comparison 1 2 3 1D array 2D array 3D array 1.5 2 3 4 5 6 Array Manipulation NumPy Arrays axis 0 axis 1 axis 0 axis 1 axis 2 Arithmetic Operations Transposing Array >>> i = np.transpose(b) Permute array dimensions >>> i.T Permute array dimensions Changing Array Shape >>> b.ravel() Flaen the array >>> g.reshape(3,-2) Reshape, but don’t change data Adding/Removing Elements >>> h.resize((2,6)) Return a new array with shape (2,6) >>> np.append(h,g) Append items to an array >>> np.insert(a, 1, 5) Insert items in an array >>> np.delete(a,[1]) Delete items from an array Combining Arrays >>> np.concatenate((a,d),axis=0) Concatenate arrays array([ 1, 2, 3, 10, 15, 20]) >>> np.vstack((a,b)) Stack arrays vertically (row-wise) array([[ 1. , 2. , 3. ], [ 1.5, 2. , 3. ], [ 4. , 5. , 6. ]]) >>> np.r_[e,f] Stack arrays vertically (row-wise) >>> np.hstack((e,f)) Stack arrays horizontally (column-wise) array([[ 7., 7., 1., 0.], [ 7., 7., 0., 1.]]) >>> np.column_stack((a,d)) Create stacked column-wise arrays array([[ 1, 10], [ 2, 15], [ 3, 20]]) >>> np.c_[a,d] Create stacked column-wise arrays Spliing Arrays >>> np.hsplit(a,3) Split the array horizontally at the 3rd [array([1]),array([2]),array([3])] index >>> np.vsplit(c,2) Split the array vertically at the 2nd index [array([[[ 1.5, 2. , 1. ], [ 4. , 5. , 6. ]]]), array([[[ 3., 2., 3.], [ 4., 5., 6.]]])] Also see Lists Subseing >>> a[2] Select the element at the 2nd index 3 >>> b[1,2] Select the element at row 1 column 2 6.0 (equivalent to b[1][2] ) Slicing >>> a[0:2] Select items at index 0 and 1 array([1, 2]) >>> b[0:2,1] Select items at rows 0 and 1 in column 1 array([ 2., 5.]) >>> b[:1] Select all items at row 0 array([[1.5, 2., 3.]]) (equivalent to b[0:1, :]) >>> c[1,...] Same as [1,:,:] array([[[ 3., 2., 1.], [ 4., 5., 6.]]]) >>> a[ : :-1] Reversed array a array([3, 2, 1]) Boolean Indexing >>> a[a<2] Select elements from a less than 2 array([1]) Fancy Indexing >>> b[[1, 0, 1, 0],[0, 1, 2, 0]] Select elements (1,0) ,(0,1) ,(1,2) and (0,0) array([ 4. , 2. , 6. , 1.5]) >>> b[[1, 0, 1, 0]][:,[0,1,2,0]] Select a subset of the matrix’s rows array([[ 4. ,5. , 6. , 4. ], and columns [ 1.5, 2. , 3. , 1.5], [ 4. , 5. , 6. , 4. ], [ 1.5, 2. , 3. , 1.5]]) >>> a = np.array([1,2,3]) >>> b = np.array([(1.5,2,3), (4,5,6)], dtype = float) >>> c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]], dtype = float) Initial Placeholders Aggregate Functions >>> np.loadtxt("myfile.txt") >>> np.genfromtxt("my_file.csv", delimiter=',') >>> np.savetxt("myarray.txt", a, delimiter=" ") I/O 1 2 3 1.5 2 3 4 5 6 Copying Arrays >>> h = a.view() Create a view of the array with the same data >>> np.copy(a) Create a copy of the array >>> h = a.copy() Create a deep copy of the array Saving & Loading Text Files Saving & Loading On Disk >>> np.save('my_array', a) >>> np.savez('array.npz', a, b) >>> np.load('my_array.npy') >>> a.shape Array dimensions >>> len(a) Length of array >>> b.ndim Number of array dimensions >>> e.size Number of array elements >>> b.dtype Data type of array elements >>> b.dtype.name Name of data type >>> b.astype(int) Convert an array to a different type Inspecting Your Array >>> np.info(np.ndarray.dtype) Asking For Help Sorting Arrays >>> a.sort() Sort an array >>> c.sort(axis=0) Sort the elements of an array's axis Data Types >>> np.int64 Signed 64-bit integer types >>> np.float32 Standard double-precision floating point >>> np.complex Complex numbers represented by 128 floats >>> np.bool Boolean type storing TRUE and FALSE values >>> np.object Python object type >>> np.string_ Fixed-length string type >>> np.unicode_ Fixed-length unicode type 1 2 3 1.5 2 3 4 5 6 1.5 2 3 4 5 6 1 2 3
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Python For Data Science Cheat SheetNumPy Basics
Learn Python for Data Science Interactively at www.DataCamp.com
NumPy
DataCampLearn Python for Data Science Interactively
The NumPy library is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.
>>> import numpy as npUse the following import convention:
Creating Arrays
>>> np.zeros((3,4)) Create an array of zeros>>> np.ones((2,3,4),dtype=np.int16) Create an array of ones>>> d = np.arange(10,25,5) Create an array of evenly spaced values (step value) >>> np.linspace(0,2,9) Create an array of evenly spaced values (number of samples)>>> e = np.full((2,2),7) Create a constant array >>> f = np.eye(2) Create a 2X2 identity matrix>>> np.random.random((2,2)) Create an array with random values>>> np.empty((3,2)) Create an empty array
Array Mathematics
>>> g = a - b Subtraction array([[-0.5, 0. , 0. ], [-3. , -3. , -3. ]])>>> np.subtract(a,b) Subtraction>>> b + a Addition array([[ 2.5, 4. , 6. ], [ 5. , 7. , 9. ]])>>> np.add(b,a) Addition>>> a / b Division array([[ 0.66666667, 1. , 1. ], [ 0.25 , 0.4 , 0.5 ]])>>> np.divide(a,b) Division>>> a * b Multiplication array([[ 1.5, 4. , 9. ], [ 4. , 10. , 18. ]])
>>> a.sum() Array-wise sum>>> a.min() Array-wise minimum value >>> b.max(axis=0) Maximum value of an array row>>> b.cumsum(axis=1) Cumulative sum of the elements>>> a.mean() Mean>>> b.median() Median>>> a.corrcoef() Correlation coefficient>>> np.std(b) Standard deviation
Comparison>>> a == b Element-wise comparison array([[False, True, True], [False, False, False]], dtype=bool)>>> a < 2 Element-wise comparison array([True, False, False], dtype=bool)>>> np.array_equal(a, b) Array-wise comparison
Changing Array Shape>>> b.ravel() Flatten the array>>> g.reshape(3,-2) Reshape, but don’t change data
Adding/Removing Elements>>> h.resize((2,6)) Return a new array with shape (2,6) >>> np.append(h,g) Append items to an array>>> np.insert(a, 1, 5) Insert items in an array>>> np.delete(a,[1]) Delete items from an array
>>> a = np.array([1,2,3])>>> b = np.array([(1.5,2,3), (4,5,6)], dtype = float)>>> c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]], dtype = float)
Initial Placeholders
Aggregate Functions
>>> np.loadtxt("myfile.txt")>>> np.genfromtxt("my_file.csv", delimiter=',')>>> np.savetxt("myarray.txt", a, delimiter=" ")
I/O
1 2 3
1.5 2 3
4 5 6
Copying Arrays>>> h = a.view() Create a view of the array with the same data>>> np.copy(a) Create a copy of the array>>> h = a.copy() Create a deep copy of the array
Saving & Loading Text Files
Saving & Loading On Disk>>> np.save('my_array', a)>>> np.savez('array.npz', a, b)>>> np.load('my_array.npy')
>>> a.shape Array dimensions>>> len(a) Length of array >>> b.ndim Number of array dimensions >>> e.size Number of array elements >>> b.dtype Data type of array elements>>> b.dtype.name Name of data type >>> b.astype(int) Convert an array to a different type
Inspecting Your Array
>>> np.info(np.ndarray.dtype)Asking For Help
Sorting Arrays>>> a.sort() Sort an array>>> c.sort(axis=0) Sort the elements of an array's axis
Data Types>>> np.int64 Signed 64-bit integer types >>> np.float32 Standard double-precision floating point>>> np.complex Complex numbers represented by 128 floats>>> np.bool Boolean type storing TRUE and FALSE values>>> np.object Python object type>>> np.string_ Fixed-length string type>>> np.unicode_ Fixed-length unicode type
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1.5 2 3
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1.5 2 3
4 5 6
1 2 3
F M A
Data Wranglingwith pandasCheat Sheet
http://pandas.pydata.org
Syntax – Creating DataFrames
Tidy Data – A foundation for wrangling in pandas
In a tidy data set:
F M A
Each variable is saved in its own column
&Each observation is saved in its own row
Tidy data complements pandas’s vectorizedoperations. pandas will automatically preserve observations as you manipulate variables. No other format works as intuitively with pandas.
Reshaping Data – Change the layout of a data set
M A F*
M A*
pd.melt(df)Gather columns into rows.
df.pivot(columns='var', values='val')Spread rows into columns.
pd.concat([df1,df2])Append rows of DataFrames
pd.concat([df1,df2], axis=1)Append columns of DataFrames
df.sort_values('mpg')Order rows by values of a column (low to high).
df.sort_values('mpg',ascending=False)Order rows by values of a column (high to low).
df.rename(columns = {'y':'year'})Rename the columns of a DataFrame
df.sort_index()Sort the index of a DataFrame
df.reset_index()Reset index of DataFrame to row numbers, moving index to columns.
df.drop(columns=['Length','Height'])Drop columns from DataFrame
index = pd.MultiIndex.from_tuples([('d',1),('d',2),('e',2)],
names=['n','v'])))Create DataFrame with a MultiIndex
Method ChainingMost pandas methods return a DataFrame so that another pandas method can be applied to the result. This improves readability of code.df = (pd.melt(df)
> Greater than df.column.isin(values) Group membership
== Equals pd.isnull(obj) Is NaN
<= Less than or equals pd.notnull(obj) Is not NaN
>= Greater than or equals &,|,~,^,df.any(),df.all() Logical and, or, not, xor, any, all
http://pandas.pydata.org/ This cheat sheet inspired by Rstudio Data Wrangling Cheatsheet (https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) Written by Irv Lustig, Princeton Consultants
df[['width','length','species']]Select multiple columns with specific names.
df['width'] or df.widthSelect single column with specific name.
df.filter(regex='regex')Select columns whose name matches regular expression regex.
df.loc[:,'x2':'x4']Select all columns between x2 and x4 (inclusive).
df.iloc[:,[1,2,5]]Select columns in positions 1, 2 and 5 (first column is 0).
df.loc[df['a'] > 10, ['a','c']]Select rows meeting logical condition, and only the specific columns .
regex (Regular Expressions) Examples
'\.' Matches strings containing a period '.'
'Length$' Matches strings ending with word 'Length'
'^Sepal' Matches strings beginning with the word 'Sepal'
'^x[1-5]$' Matches strings beginning with 'x' and ending with 1,2,3,4,5
''^(?!Species$).*' Matches strings except the string 'Species'
df.sample(frac=0.5)Randomly select fraction of rows.
df.sample(n=10)Randomly select n rows.
df.iloc[10:20]Select rows by position.
df.nlargest(n, 'value')Select and order top n entries.
df.nsmallest(n, 'value')Select and order bottom n entries.
Count number of rows with each unique value of variablelen(df)
# of rows in DataFrame.df['w'].nunique()
# of distinct values in a column.df.describe()
Basic descriptive statistics for each column (or GroupBy)
pandas provides a large set of summary functions that operate on different kinds of pandas objects (DataFrame columns, Series, GroupBy, Expanding and Rolling (see below)) and produce single values for each of the groups. When applied to a DataFrame, the result is returned as a pandas Series for each column. Examples:
sum()Sum values of each object.
count()Count non-NA/null values of each object.
median()Median value of each object.
quantile([0.25,0.75])Quantiles of each object.
apply(function)Apply function to each object.
min()Minimum value in each object.
max()Maximum value in each object.
mean()Mean value of each object.
var()Variance of each object.
std()Standard deviation of each object.
df.assign(Area=lambda df: df.Length*df.Height)Compute and append one or more new columns.
df['Volume'] = df.Length*df.Height*df.DepthAdd single column.
pd.qcut(df.col, n, labels=False)Bin column into n buckets.
Vector function
Vector function
pandas provides a large set of vector functions that operate on all columns of a DataFrame or a single selected column (a pandas Series). These functions produce vectors of values for each of the columns, or a single Series for the individual Series. Examples:
shift(1)Copy with values shifted by 1.
rank(method='dense')Ranks with no gaps.
rank(method='min')Ranks. Ties get min rank.
rank(pct=True)Ranks rescaled to interval [0, 1].
rank(method='first')Ranks. Ties go to first value.
shift(-1)Copy with values lagged by 1.
cumsum()Cumulative sum.
cummax()Cumulative max.
cummin()Cumulative min.
cumprod()Cumulative product.
x1 x2A 1B 2C 3
x1 x3A TB FD T
adf bdf
Standard Joins
x1 x2 x3A 1 TB 2 FC 3 NaN
x1 x2 x3A 1.0 TB 2.0 FD NaN T
x1 x2 x3A 1 TB 2 F
x1 x2 x3A 1 TB 2 FC 3 NaND NaN T
pd.merge(adf, bdf,how='left', on='x1')
Join matching rows from bdf to adf.
pd.merge(adf, bdf,how='right', on='x1')
Join matching rows from adf to bdf.
pd.merge(adf, bdf,how='inner', on='x1')
Join data. Retain only rows in both sets.
pd.merge(adf, bdf,how='outer', on='x1')
Join data. Retain all values, all rows.
Filtering Joins
x1 x2A 1B 2
x1 x2C 3
adf[adf.x1.isin(bdf.x1)]All rows in adf that have a match in bdf.
adf[~adf.x1.isin(bdf.x1)]All rows in adf that do not have a match in bdf.
x1 x2A 1B 2C 3
x1 x2B 2C 3D 4
ydf zdf
Set-like Operations
x1 x2B 2C 3
x1 x2A 1B 2C 3D 4
x1 x2A 1
pd.merge(ydf, zdf)Rows that appear in both ydf and zdf(Intersection).
pd.merge(ydf, zdf, how='outer')Rows that appear in either or both ydf and zdf(Union).
pd.merge(ydf, zdf, how='outer', indicator=True)
.query('_merge == "left_only"')
.drop(columns=['_merge'])Rows that appear in ydf but not zdf (Setdiff).
Group Datadf.groupby(by="col")
Return a GroupBy object, grouped by values in column named "col".
df.groupby(level="ind")Return a GroupBy object, grouped by values in index level named "ind".
All of the summary functions listed above can be applied to a group. Additional GroupBy functions:
max(axis=1)Element-wise max.
clip(lower=-10,upper=10)Trim values at input thresholds
min(axis=1)Element-wise min.
abs()Absolute value.
The examples below can also be applied to groups. In this case, the function is applied on a per-group basis, and the returned vectors are of the length of the original DataFrame.
Windowsdf.expanding()
Return an Expanding object allowing summary functions to be applied cumulatively.
df.rolling(n)Return a Rolling object allowing summary functions to be applied to windows of length n.
size()Size of each group.
agg(function)Aggregate group using function.
Handling Missing Datadf.dropna()
Drop rows with any column having NA/null data.df.fillna(value)
Replace all NA/null data with value.
Plottingdf.plot.hist()
Histogram for each columndf.plot.scatter(x='w',y='h')
Scatter chart using pairs of points
http://pandas.pydata.org/ This cheat sheet inspired by Rstudio Data Wrangling Cheatsheet (https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) Written by Irv Lustig, Princeton Consultants
DataCampLearn Python for Data Science Interactively
Prepare The Data Also see Lists & NumPy
Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
1>>> import numpy as np>>> x = np.linspace(0, 10, 100)>>> y = np.cos(x) >>> z = np.sin(x)
Show Plot>>> plt.show()
Save Plot Save figures>>> plt.savefig('foo.png') Save transparent figures>>> plt.savefig('foo.png', transparent=True)
All plotting is done with respect to an Axes. In most cases, a subplot will fit your needs. A subplot is an axes on a grid system.>>> fig.add_axes()>>> ax1 = fig.add_subplot(221) # row-col-num>>> ax3 = fig.add_subplot(212) >>> fig3, axes = plt.subplots(nrows=2,ncols=2)>>> fig4, axes2 = plt.subplots(ncols=3)
Customize PlotColors, Color Bars & Color Maps
Markers
Linestyles
Mathtext
Text & Annotations
Limits, Legends & Layouts
The basic steps to creating plots with matplotlib are: 1 Prepare data 2 Create plot 3 Plot 4 Customize plot 5 Save plot 6 Show plot
>>> import matplotlib.pyplot as plt>>> x = [1,2,3,4]>>> y = [10,20,25,30]>>> fig = plt.figure()>>> ax = fig.add_subplot(111)>>> ax.plot(x, y, color='lightblue', linewidth=3)>>> ax.scatter([2,4,6], [5,15,25], color='darkgreen', marker='^')>>> ax.set_xlim(1, 6.5)>>> plt.savefig('foo.png')>>> plt.show()
Step 3, 4
Step 2
Step 1
Step 3
Step 6
Plot Anatomy Workflow
4
Limits & Autoscaling>>> ax.margins(x=0.0,y=0.1) Add padding to a plot>>> ax.axis('equal') Set the aspect ratio of the plot to 1>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5]) Set limits for x-and y-axis>>> ax.set_xlim(0,10.5) Set limits for x-axis Legends>>> ax.set(title='An Example Axes', Set a title and x-and y-axis labels ylabel='Y-Axis', xlabel='X-Axis')>>> ax.legend(loc='best') No overlapping plot elements Ticks>>> ax.xaxis.set(ticks=range(1,5), Manually set x-ticks ticklabels=[3,100,-12,"foo"])>>> ax.tick_params(axis='y', Make y-ticks longer and go in and out direction='inout', length=10)
Subplot Spacing>>> fig3.subplots_adjust(wspace=0.5, Adjust the spacing between subplots hspace=0.3, left=0.125, right=0.9, top=0.9, bottom=0.1)>>> fig.tight_layout() Fit subplot(s) in to the figure area Axis Spines>>> ax1.spines['top'].set_visible(False) Make the top axis line for a plot invisible>>> ax1.spines['bottom'].set_position(('outward',10)) Move the bottom axis line outward
Figure
Axes
>>> data = 2 * np.random.random((10, 10))>>> data2 = 3 * np.random.random((10, 10))>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]>>> U = -1 - X**2 + Y>>> V = 1 + X - Y**2>>> from matplotlib.cbook import get_sample_data>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))
>>> lines = ax.plot(x,y) Draw points with lines or markers connecting them>>> ax.scatter(x,y) Draw unconnected points, scaled or colored>>> axes[0,0].bar([1,2,3],[3,4,5]) Plot vertical rectangles (constant width) >>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) Plot horiontal rectangles (constant height)>>> axes[1,1].axhline(0.45) Draw a horizontal line across axes >>> axes[0,1].axvline(0.65) Draw a vertical line across axes>>> ax.fill(x,y,color='blue') Draw filled polygons >>> ax.fill_between(x,y,color='yellow') Fill between y-values and 0
Plotting Routines31D Data
>>> fig, ax = plt.subplots()>>> im = ax.imshow(img, Colormapped or RGB arrays cmap='gist_earth', interpolation='nearest', vmin=-2, vmax=2)
2D Data or Images
Vector Fields>>> axes[0,1].arrow(0,0,0.5,0.5) Add an arrow to the axes>>> axes[1,1].quiver(y,z) Plot a 2D field of arrows>>> axes[0,1].streamplot(X,Y,U,V) Plot 2D vector fields
Data Distributions>>> ax1.hist(y) Plot a histogram>>> ax3.boxplot(y) Make a box and whisker plot>>> ax3.violinplot(z) Make a violin plot
>>> axes2[0].pcolor(data2) Pseudocolor plot of 2D array>>> axes2[0].pcolormesh(data) Pseudocolor plot of 2D array>>> CS = plt.contour(Y,X,U) Plot contours>>> axes2[2].contourf(data1) Plot filled contours>>> axes2[2]= ax.clabel(CS) Label a contour plot
Figure
Axes/Subplot
Y-axis
X-axis
1D Data
2D Data or Images
>>> plt.plot(x, x, x, x**2, x, x**3)>>> ax.plot(x, y, alpha = 0.4)>>> ax.plot(x, y, c='k')>>> fig.colorbar(im, orientation='horizontal')>>> im = ax.imshow(img, cmap='seismic')
Close & Clear >>> plt.cla() Clear an axis>>> plt.clf() Clear the entire figure>>> plt.close() Close a window
Python For Data Science Cheat SheetScikit-Learn
Learn Python for data science Interactively at www.DataCamp.com
Scikit-learn
DataCampLearn Python for Data Science Interactively
Loading The Data Also see NumPy & Pandas
Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface.
>>> import numpy as np>>> X = np.random.random((10,5))>>> y = np.array(['M','M','F','F','M','F','M','M','F','F','F'])>>> X[X < 0.7] = 0
Your data needs to be numeric and stored as NumPy arrays or SciPy sparse matrices. Other types that are convertible to numeric arrays, such as Pandas DataFrame, are also acceptable.
Create Your Model
Model Fitting
Prediction
Tune Your Model
Evaluate Your Model’s Performance
Grid Search
Randomized Parameter Optimization
Linear Regression>>> from sklearn.linear_model import LinearRegression>>> lr = LinearRegression(normalize=True)
Support Vector Machines (SVM)>>> from sklearn.svm import SVC>>> svc = SVC(kernel='linear') Naive Bayes >>> from sklearn.naive_bayes import GaussianNB>>> gnb = GaussianNB()
KNN>>> from sklearn import neighbors>>> knn = neighbors.KNeighborsClassifier(n_neighbors=5)
Training And Test Data>>> from sklearn.model_selection import train_test_split>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)